Creativity is thought to require the flexible reconfiguration of multiple brain regions that interact in transient and complex communication patterns. In contrast to prior emphases on searching for specific regions or networks associated with creative performance, we focused on exploring the association between the reconfiguration of dynamic functional connectivity states and creative ability. We hypothesized that a high frequency of dynamic functional connectivity state transitions will be associated with creative ability. To test this hypothesis, we recruited a high-creative group (HCG) and a low-creative group (LCG) of participants and collected resting-state fMRI (R-fMRI) data and Torrance Tests of Creative Thinking (TTCT) scores from each participant. By combining an independent component analysis with a dynamic network analysis approach, we discovered the HCG had more frequent transitions between dynamic functional connectivity (dFC) states than the LCG. Moreover, a confirmatory analysis using multiplication of temporal derivatives also indicated that there were more frequent dFC state transitions in the HCG. Taken together, these results provided empirical evidence for a linkage between the flexible reconfiguration of dynamic functional connectivity states and creative ability. These findings have the potential to provide new insights into the neural basis of creativity.

f4: Group differences in the transition frequency of the dynamic functional connectivity states.(a) The dynamic FC states were estimated using sliding window approach; (b) The dynamic FC states were estimated using Multiplication of Temporal Derivatives (MTD). The ticks on the horizontal axis indicate the state transition pairs, e.g., “1–2” refers to the transitions between state 1 and state 2. The state transition times of each individual in high-creative and low-creative group are presented in red and green, respectively. For each group, the black line indicates the mean state transition times of that group, and the light gray rectangle covers the data within one standard deviation above and below the mean. The pairs of groups with asterisks indicates that there are statistically significant differences between them.

Mentions:
In this study, the properties of the dFC of each subject were depicted by two metrics, the reoccurrence times of all the dFC states and the dFC transition frequency between all the pairs of dFC states. For each subject, the reoccurrence time for each dFC state was defined as the total number of dFC windows assigned to that state, and the dFC transition frequency counts how many times the dFC windows altered their allegiance between a pair of states (e.g., the transition frequency between dFC state 1 and state 2 counted if the dFC windows altered their allegiance from state 1 to state 2 or from state 2 to state 1). Chi-square tests revealed significant differences in the reoccurrence times of the dFC states between the two groups (Fig. 3), χ2 (3, N = 44) = 423.01, p < 0.01. A post hoc analysis further indicated that the HCG had an increase in the reoccurrence times for state 3 but a decrease in the reoccurrence times for state 1 and state 2 compared to the reoccurrence times of the LCG (FDR corrected, α = 0.05). In addition, there were significant differences in the transition frequency of the dFC states between the two groups (Fig. 4a), χ2 (5, N = 44) = 14.97, p = 0.01. A post hoc analysis revealed that, compared with the LCG, the HCG had significantly more frequent transitions between state 1 and state 3 and between state 3 and state 4 (p < 0.05, uncorrected). To evaluate the robustness of our results, we performed exploratory analyses of k = 3 and 5 (please see supplementary information for further information). We obtained similar results, in that the high creativity group (HCG) had more frequent transitions between dynamic functional connectivity (dFC) states than did the low creativity group (LCG).

f4: Group differences in the transition frequency of the dynamic functional connectivity states.(a) The dynamic FC states were estimated using sliding window approach; (b) The dynamic FC states were estimated using Multiplication of Temporal Derivatives (MTD). The ticks on the horizontal axis indicate the state transition pairs, e.g., “1–2” refers to the transitions between state 1 and state 2. The state transition times of each individual in high-creative and low-creative group are presented in red and green, respectively. For each group, the black line indicates the mean state transition times of that group, and the light gray rectangle covers the data within one standard deviation above and below the mean. The pairs of groups with asterisks indicates that there are statistically significant differences between them.

Mentions:
In this study, the properties of the dFC of each subject were depicted by two metrics, the reoccurrence times of all the dFC states and the dFC transition frequency between all the pairs of dFC states. For each subject, the reoccurrence time for each dFC state was defined as the total number of dFC windows assigned to that state, and the dFC transition frequency counts how many times the dFC windows altered their allegiance between a pair of states (e.g., the transition frequency between dFC state 1 and state 2 counted if the dFC windows altered their allegiance from state 1 to state 2 or from state 2 to state 1). Chi-square tests revealed significant differences in the reoccurrence times of the dFC states between the two groups (Fig. 3), χ2 (3, N = 44) = 423.01, p < 0.01. A post hoc analysis further indicated that the HCG had an increase in the reoccurrence times for state 3 but a decrease in the reoccurrence times for state 1 and state 2 compared to the reoccurrence times of the LCG (FDR corrected, α = 0.05). In addition, there were significant differences in the transition frequency of the dFC states between the two groups (Fig. 4a), χ2 (5, N = 44) = 14.97, p = 0.01. A post hoc analysis revealed that, compared with the LCG, the HCG had significantly more frequent transitions between state 1 and state 3 and between state 3 and state 4 (p < 0.05, uncorrected). To evaluate the robustness of our results, we performed exploratory analyses of k = 3 and 5 (please see supplementary information for further information). We obtained similar results, in that the high creativity group (HCG) had more frequent transitions between dynamic functional connectivity (dFC) states than did the low creativity group (LCG).

Creativity is thought to require the flexible reconfiguration of multiple brain regions that interact in transient and complex communication patterns. In contrast to prior emphases on searching for specific regions or networks associated with creative performance, we focused on exploring the association between the reconfiguration of dynamic functional connectivity states and creative ability. We hypothesized that a high frequency of dynamic functional connectivity state transitions will be associated with creative ability. To test this hypothesis, we recruited a high-creative group (HCG) and a low-creative group (LCG) of participants and collected resting-state fMRI (R-fMRI) data and Torrance Tests of Creative Thinking (TTCT) scores from each participant. By combining an independent component analysis with a dynamic network analysis approach, we discovered the HCG had more frequent transitions between dynamic functional connectivity (dFC) states than the LCG. Moreover, a confirmatory analysis using multiplication of temporal derivatives also indicated that there were more frequent dFC state transitions in the HCG. Taken together, these results provided empirical evidence for a linkage between the flexible reconfiguration of dynamic functional connectivity states and creative ability. These findings have the potential to provide new insights into the neural basis of creativity.